2020 excess mortality & voting patterns in CH

Redistributed cantonal deaths

Data

Spatial

kt = read_rds("data/BfS/kt.Rds")
gg = read_rds("data/BfS/gg.Rds")
tg3o = read_rds("data/BfS/tg3o.Rds")
se_alt = read_rds("data/BfS/se_alt.Rds")

Downscaled data

exp_deaths_2020_year_gem = read_rds("results/exp_deaths_2020_year_gem.Rds") %>% 
  select(-munici_excess_pop)

Note the NA!

summary(exp_deaths_2020_year_gem$munici_excess_rat)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.3058  0.4167  0.4964  0.5714  4.0000     101

Ratio of excess deaths to expected deaths

Distribution

Maps

x <categorical> 
# total N=2141 valid N=2040 mean=2.98 sd=1.43

Value         |   N | Raw % | Valid % | Cum. %
----------------------------------------------
[0.000,0.287) | 436 | 20.36 |   21.37 |  21.37
[0.287,0.365) | 389 | 18.17 |   19.07 |  40.44
[0.365,0.483) | 399 | 18.64 |   19.56 |  60.00
[0.483,0.625) | 409 | 19.10 |   20.05 |  80.05
[0.625,4.000] | 407 | 19.01 |   19.95 | 100.00
<NA>          | 101 |  4.72 |    <NA> |   <NA>

Choropleth

Proportional symbols

Symbol size perceptually scaled to number of expected deaths.

EDA June vote

Map

Correlations

Unweighted

cor.test(exp_deaths_2020_year_gem$munici_excess_rat, 
         exp_deaths_2020_year_gem$vote_yes_jun_perc, 
         method = "pearson")


    Pearson's product-moment correlation

data:  exp_deaths_2020_year_gem$munici_excess_rat and exp_deaths_2020_year_gem$vote_yes_jun_perc
t = 5.0156, df = 2038, p-value = 0.0000005745
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.06734542 0.15308706
sample estimates:
      cor 
0.1104217

Weighted

wtd.cor(exp_deaths_2020_year_gem$munici_excess_rat, 
        exp_deaths_2020_year_gem$vote_yes_jun_perc, 
        weight = exp_deaths_2020_year_gem$munici_expected_med)

  correlation    std.err   t.value   p.value
Y -0.02421592 0.02214474 -1.093529 0.2742907

Scatter

Unweighted

Weighted

Box

EDA Nov vote

Map

Correlations

Unweighted

cor.test(exp_deaths_2020_year_gem$munici_excess_rat, 
         exp_deaths_2020_year_gem$vote_yes_nov_perc, 
         method = "pearson")


    Pearson's product-moment correlation

data:  exp_deaths_2020_year_gem$munici_excess_rat and exp_deaths_2020_year_gem$vote_yes_nov_perc
t = 1.8844, df = 2038, p-value = 0.05965
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.001696124  0.084951179
sample estimates:
       cor 
0.04170594

Weighted

wtd.cor(exp_deaths_2020_year_gem$munici_excess_rat, 
        exp_deaths_2020_year_gem$vote_yes_nov_perc, 
        weight = exp_deaths_2020_year_gem$munici_expected_med)

  correlation    std.err   t.value               p.value
Y  -0.1597816 0.02186664 -7.307091 0.0000000000003899328